Biology subjects

Kremling, K. A.

Publications and source records attributed to Kremling, K. A..

4 recordsLinked to original sources

bioRxiv2022

Cross-species predictive modeling reveals conserved drought responses between maize and sorghum

Drought tolerance is a highly complex trait controlled by numerous interconnected pathways with substantial variation within and across plant species. This complexity makes it difficult to distill individual genetic loci underlying tolerance, and to identify core or conserved drought responsive pathways. Here, we collected drought physiology and gene expression datasets across diverse genotypes of the C4 cereals sorghum and maize and searched for signatures defining water deficit responses. Differential gene expression identified few overlapping drought associated genes across sorghum genotypes, but using a predictive modeling approach, we found a shared core drought response across development, genotype, and stress severity. Our model had similar robustness when applied to datasets in maize, reflecting a conserved drought response between sorghum and maize. The top predictors are enriched in functions associated with various abiotic stress responsive pathways as well as core cellular functions. These conserved drought response genes were less likely to contain deleterious mutations than other gene sets, suggesting that core drought responsive genes are under evolutionary and functional constraints. Our findings support a broad evolutionary conservation of drought responses in C4 grasses regardless of innate stress tolerance, which could have important implications for developing climate resilient cereals. Significance StatementDrought is a complex and variable stress that is difficult to quantify and link to underlying mechanisms both within and across species. Here, we developed a predictive model to classify drought stress responses in sorghum and identify important features that are responsive to water deficit. Our model has high predictive accuracy across development, genotype, and stress severity, and the top features are enriched in genes related to classical stress responses and have functional and evolutionary conservation. We applied this sorghum trained model to maize, and observed similar predictive accuracy of drought responses, supporting transfer learning across plant species. Our findings suggest there are deeply conserved drought responses across C4 grasses that are unrelated to tolerance.

plant biology↗

bioRxiv2018

Evolutionarily informed deep learning methods: Predicting transcript abundance from DNA sequence

Deep learning methodologies have revolutionized prediction in many fields, and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two novel approaches that account for evolutionary relatedness in machine learning models: 1) gene-family guided splitting, and 2) ortholog contrasts. The first approach accounts for evolution by constraining the models training and testing sets to include different gene families. The second, uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two approaches were explored and validated within the context of mRNA expression level prediction, and have prediction auROC values ranging from 0.72 to 0.94. Model weight inspections showed biologically interpretable patterns, resulting in the novel hypothesis that the 3 UTR is more important for fine tuning mRNA abundance levels while the 5 UTR is more important for large scale changes.

molecular biology↗

bioRxiv2018

Ethylene signaling regulates natural variation in the abundance of antifungal acetylated diferuloylsucroses and Fusarium graminearum resistance in maize seedling roots

O_LIThe production and regulation of defensive specialized metabolites plays a central role in pathogen resistance in maize (Zea mays) and other plants. Therefore, identification of genes involved in plant specialized metabolism can contribute to improved disease resistance.\nC_LIO_LIWe used comparative metabolomics to identify previously unknown antifungal metabolites in maize seedling roots, and investigated the genetic and physiological mechanisms underlying their natural variation using quantitative trait locus (QTL) mapping and comparative transcriptomics approaches.\nC_LIO_LITwo maize metabolites, smilaside A (3,6-diferuloyl-3',6'-diacetylsucrose) and smiglaside C (3,6-diferuloyl-2',3',6'-triacetylsucrose), that may contribute to maize resistance against Fusarium graminearum and other fungal pathogens were identified. Elevated expression of an ethylene receptor gene, ETHYLENE INSENSITIVE 2 (ZmEIN2), co-segregated with decreased smilaside A/smiglaside C ratio. Pharmacological and genetic manipulation of ethylene availability and sensitivity in vivo indicated that, whereas ethylene was required for the production of both metabolites, the smilaside A/smiglaside C ratio was negatively regulated by ethylene sensitivity. This ratio, rather than the absolute abundance of these two metabolites, was important for maize seedling root defense against F. graminearum.\nC_LIO_LIEthylene signaling regulates the relative abundance of the two F. graminearum-resistance-related metabolites and affects resistance against F. graminearum in maize seedling roots.\nC_LI

plant biology↗

bioRxiv2018

Quantitative Genetic Analysis of the Maize Leaf Microbiome

The degree to which an organism can affect its associated microbial communities (\"microbiome\") varies by organism and habitat, and in many cases is unknown. We address this question by analyzing the metabolically active bacteria of the maize phyllosphere across 300 diverse maize lines growing in a common environment. We performed comprehensive heritability analysis for 49 community diversity metrics, 380 bacterial clades (individual operational taxonomic units and higher-level groupings), and 9042 predicted metagenomic functions. We find that only a few few bacterial clades (5) and diversity metrics (2) are significantly heritable, while a much larger number of metabolic functions (200) are. Many of these associations appear to be driven by the amount of Methylobacteria present in each sample, and we find significant enrichment for traits relating to short-chain carbon metabolism, secretion, and nitrotoluene degradation. Genome-wide association analysis identifies a small number of associated loci for these heritable traits, including two loci (on maize chromosomes 7 and 10) that affect a large number of traits even after correcting for correlations among traits. This work is among the most comprehensive analyses of the maize phyllosphere to date. Our results indicate that while most of the maize phyllosphere composition is driven by environmental factors and/or stochastic founder events, a subset of bacterial taxa and metabolic functions is nonetheless significantly impacted by host plant genetics. Additional work will be needed to identify the exact nature of these interactions and what effects they may have on the phenotype of host plants.

genetics↗